Overview

Dataset statistics

Number of variables16
Number of observations50
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.4 KiB
Average record size in memory130.6 B

Variable types

Text3
Numeric11
Categorical2

Alerts

energy is highly overall correlated with loudness and 1 other fieldsHigh correlation
loudness is highly overall correlated with energyHigh correlation
speechiness is highly overall correlated with energyHigh correlation
time_signature is highly imbalanced (75.8%)Imbalance
Track_ID has unique valuesUnique
Track_Name has unique valuesUnique
energy has unique valuesUnique
loudness has unique valuesUnique
speechiness has unique valuesUnique
liveness has unique valuesUnique
tempo has unique valuesUnique
duration_ms has unique valuesUnique
key has 3 (6.0%) zerosZeros
instrumentalness has 34 (68.0%) zerosZeros

Reproduction

Analysis started2023-11-24 07:49:38.298413
Analysis finished2023-11-24 07:49:59.709378
Duration21.41 seconds
Software versionydata-profiling vv4.6.2
Download configurationconfig.json

Variables

Track_ID
Text

UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
2023-11-24T10:49:59.992069image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters1100
Distinct characters62
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50 ?
Unique (%)100.0%

Sample

1st row1rDgAHDX95RmylxjgVW9tN
2nd row0GWNtMohuYUEHVZ40tcnHF
3rd row2HRgqmZQC0MC7GeNuDIXHN
4th row5i8lwhzx9FyilInJWa5lhn
5th row1hAloWiinXLPQUJxrJReb1
ValueCountFrequency (%)
1rdgahdx95rmylxjgvw9tn 1
 
2.0%
2cxcqkwqtfkq7giphxjzou 1
 
2.0%
01qfknwq73ufesli0gvume 1
 
2.0%
2hrgqmzqc0mc7genudixhn 1
 
2.0%
5i8lwhzx9fyilinjwa5lhn 1
 
2.0%
1halowiinxlpqujxrjreb1 1
 
2.0%
74x2u8jmvoog2qbjrxxwr8 1
 
2.0%
0ytm7bcx451c6lqbkddy4q 1
 
2.0%
3gq19wo6cbbpdhymt2gvt0 1
 
2.0%
12uspu2wnqgchaw1ek2dfd 1
 
2.0%
Other values (40) 40
80.0%
2023-11-24T10:50:00.547051image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 33
 
3.0%
2 26
 
2.4%
4 24
 
2.2%
t 24
 
2.2%
q 23
 
2.1%
0 23
 
2.1%
W 22
 
2.0%
9 22
 
2.0%
T 22
 
2.0%
o 22
 
2.0%
Other values (52) 859
78.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 444
40.4%
Uppercase Letter 444
40.4%
Decimal Number 212
19.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 24
 
5.4%
q 23
 
5.2%
o 22
 
5.0%
e 20
 
4.5%
u 20
 
4.5%
y 20
 
4.5%
r 20
 
4.5%
b 19
 
4.3%
c 19
 
4.3%
m 19
 
4.3%
Other values (16) 238
53.6%
Uppercase Letter
ValueCountFrequency (%)
W 22
 
5.0%
T 22
 
5.0%
N 22
 
5.0%
M 22
 
5.0%
Q 21
 
4.7%
O 20
 
4.5%
J 20
 
4.5%
L 19
 
4.3%
D 19
 
4.3%
H 19
 
4.3%
Other values (16) 238
53.6%
Decimal Number
ValueCountFrequency (%)
1 33
15.6%
2 26
12.3%
4 24
11.3%
0 23
10.8%
9 22
10.4%
7 21
9.9%
6 19
9.0%
3 17
8.0%
5 15
7.1%
8 12
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 888
80.7%
Common 212
 
19.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 24
 
2.7%
q 23
 
2.6%
W 22
 
2.5%
T 22
 
2.5%
o 22
 
2.5%
N 22
 
2.5%
M 22
 
2.5%
Q 21
 
2.4%
e 20
 
2.3%
u 20
 
2.3%
Other values (42) 670
75.5%
Common
ValueCountFrequency (%)
1 33
15.6%
2 26
12.3%
4 24
11.3%
0 23
10.8%
9 22
10.4%
7 21
9.9%
6 19
9.0%
3 17
8.0%
5 15
7.1%
8 12
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1100
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 33
 
3.0%
2 26
 
2.4%
4 24
 
2.2%
t 24
 
2.2%
q 23
 
2.1%
0 23
 
2.1%
W 22
 
2.0%
9 22
 
2.0%
T 22
 
2.0%
o 22
 
2.0%
Other values (52) 859
78.1%

Track_Name
Text

UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
2023-11-24T10:50:00.892094image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length35
Median length16
Mean length8.44
Min length1

Characters and Unicode

Total characters422
Distinct characters130
Distinct categories10 ?
Distinct scripts5 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50 ?
Unique (%)100.0%

Sample

1st rowShow
2nd rowSPECIALZ
3rd rowSeven (feat. Latto) (Explicit Ver.)
4th rowrendez-vous
5th rowアイドル
ValueCountFrequency (%)
3
 
3.7%
feat 2
 
2.4%
kura 2
 
2.4%
night 1
 
1.2%
hana 1
 
1.2%
青のすみか 1
 
1.2%
hanauta 1
 
1.2%
no 1
 
1.2%
kaiju 1
 
1.2%
勇者 1
 
1.2%
Other values (68) 68
82.9%
2023-11-24T10:50:01.461246image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
32
 
7.6%
e 28
 
6.6%
a 26
 
6.2%
o 18
 
4.3%
n 18
 
4.3%
t 17
 
4.0%
i 16
 
3.8%
r 14
 
3.3%
S 11
 
2.6%
s 9
 
2.1%
Other values (120) 233
55.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 219
51.9%
Other Letter 89
21.1%
Uppercase Letter 64
 
15.2%
Space Separator 32
 
7.6%
Other Punctuation 7
 
1.7%
Open Punctuation 3
 
0.7%
Close Punctuation 3
 
0.7%
Dash Punctuation 2
 
0.5%
Modifier Letter 2
 
0.5%
Decimal Number 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
4
 
4.5%
3
 
3.4%
3
 
3.4%
2
 
2.2%
2
 
2.2%
2
 
2.2%
2
 
2.2%
2
 
2.2%
2
 
2.2%
2
 
2.2%
Other values (62) 65
73.0%
Lowercase Letter
ValueCountFrequency (%)
e 28
12.8%
a 26
11.9%
o 18
 
8.2%
n 18
 
8.2%
t 17
 
7.8%
i 16
 
7.3%
r 14
 
6.4%
s 9
 
4.1%
u 9
 
4.1%
c 8
 
3.7%
Other values (15) 56
25.6%
Uppercase Letter
ValueCountFrequency (%)
S 11
17.2%
L 7
 
10.9%
A 6
 
9.4%
N 4
 
6.2%
H 4
 
6.2%
I 3
 
4.7%
E 3
 
4.7%
K 3
 
4.7%
G 3
 
4.7%
T 2
 
3.1%
Other values (13) 18
28.1%
Other Punctuation
ValueCountFrequency (%)
. 3
42.9%
/ 2
28.6%
' 1
 
14.3%
1
 
14.3%
Space Separator
ValueCountFrequency (%)
32
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Modifier Letter
ValueCountFrequency (%)
2
100.0%
Decimal Number
ValueCountFrequency (%)
3 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 283
67.1%
Common 50
 
11.8%
Han 35
 
8.3%
Katakana 34
 
8.1%
Hiragana 20
 
4.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 28
 
9.9%
a 26
 
9.2%
o 18
 
6.4%
n 18
 
6.4%
t 17
 
6.0%
i 16
 
5.7%
r 14
 
4.9%
S 11
 
3.9%
s 9
 
3.2%
u 9
 
3.2%
Other values (38) 117
41.3%
Han
ValueCountFrequency (%)
3
 
8.6%
2
 
5.7%
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
Other values (22) 22
62.9%
Katakana
ValueCountFrequency (%)
4
 
11.8%
3
 
8.8%
2
 
5.9%
2
 
5.9%
2
 
5.9%
2
 
5.9%
2
 
5.9%
2
 
5.9%
1
 
2.9%
1
 
2.9%
Other values (13) 13
38.2%
Hiragana
ValueCountFrequency (%)
2
 
10.0%
2
 
10.0%
2
 
10.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
Other values (7) 7
35.0%
Common
ValueCountFrequency (%)
32
64.0%
( 3
 
6.0%
. 3
 
6.0%
) 3
 
6.0%
/ 2
 
4.0%
- 2
 
4.0%
2
 
4.0%
' 1
 
2.0%
1
 
2.0%
3 1
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 330
78.2%
Katakana 36
 
8.5%
CJK 35
 
8.3%
Hiragana 20
 
4.7%
None 1
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
32
 
9.7%
e 28
 
8.5%
a 26
 
7.9%
o 18
 
5.5%
n 18
 
5.5%
t 17
 
5.2%
i 16
 
4.8%
r 14
 
4.2%
S 11
 
3.3%
s 9
 
2.7%
Other values (46) 141
42.7%
Katakana
ValueCountFrequency (%)
4
 
11.1%
3
 
8.3%
2
 
5.6%
2
 
5.6%
2
 
5.6%
2
 
5.6%
2
 
5.6%
2
 
5.6%
2
 
5.6%
1
 
2.8%
Other values (14) 14
38.9%
CJK
ValueCountFrequency (%)
3
 
8.6%
2
 
5.7%
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
Other values (22) 22
62.9%
Hiragana
ValueCountFrequency (%)
2
 
10.0%
2
 
10.0%
2
 
10.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
Other values (7) 7
35.0%
None
ValueCountFrequency (%)
1
100.0%
Distinct35
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
2023-11-24T10:50:01.763776image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length36
Median length20
Mean length14.7
Min length7

Characters and Unicode

Total characters735
Distinct characters66
Distinct categories9 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)56.0%

Sample

1st row['Ado']
2nd row['King Gnu']
3rd row['Jung Kook', 'Latto']
4th row['shy taupe']
5th row['YOASOBI']
ValueCountFrequency (%)
mrs 6
 
6.4%
apple 6
 
6.4%
green 6
 
6.4%
yoasobi 4
 
4.3%
hige 3
 
3.2%
kook 3
 
3.2%
dandism 3
 
3.2%
jung 3
 
3.2%
official 3
 
3.2%
yuuri 3
 
3.2%
Other values (49) 54
57.4%
2023-11-24T10:50:02.294049image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 106
 
14.4%
[ 50
 
6.8%
] 50
 
6.8%
44
 
6.0%
u 28
 
3.8%
E 28
 
3.8%
i 24
 
3.3%
I 23
 
3.1%
a 23
 
3.1%
A 22
 
3.0%
Other values (56) 337
45.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 245
33.3%
Lowercase Letter 214
29.1%
Other Punctuation 117
15.9%
Open Punctuation 50
 
6.8%
Close Punctuation 50
 
6.8%
Space Separator 44
 
6.0%
Other Letter 12
 
1.6%
Decimal Number 2
 
0.3%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 28
 
11.4%
I 23
 
9.4%
A 22
 
9.0%
S 19
 
7.8%
O 14
 
5.7%
P 13
 
5.3%
N 12
 
4.9%
M 12
 
4.9%
F 12
 
4.9%
Y 11
 
4.5%
Other values (14) 79
32.2%
Lowercase Letter
ValueCountFrequency (%)
u 28
13.1%
i 24
11.2%
a 23
10.7%
o 20
9.3%
n 18
8.4%
r 14
 
6.5%
s 14
 
6.5%
t 10
 
4.7%
k 10
 
4.7%
e 9
 
4.2%
Other values (12) 44
20.6%
Other Letter
ValueCountFrequency (%)
2
16.7%
2
16.7%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
Other Punctuation
ValueCountFrequency (%)
' 106
90.6%
. 7
 
6.0%
, 3
 
2.6%
: 1
 
0.9%
Decimal Number
ValueCountFrequency (%)
0 1
50.0%
1 1
50.0%
Open Punctuation
ValueCountFrequency (%)
[ 50
100.0%
Close Punctuation
ValueCountFrequency (%)
] 50
100.0%
Space Separator
ValueCountFrequency (%)
44
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 459
62.4%
Common 264
35.9%
Katakana 8
 
1.1%
Han 4
 
0.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
u 28
 
6.1%
E 28
 
6.1%
i 24
 
5.2%
I 23
 
5.0%
a 23
 
5.0%
A 22
 
4.8%
o 20
 
4.4%
S 19
 
4.1%
n 18
 
3.9%
O 14
 
3.1%
Other values (36) 240
52.3%
Common
ValueCountFrequency (%)
' 106
40.2%
[ 50
18.9%
] 50
18.9%
44
16.7%
. 7
 
2.7%
, 3
 
1.1%
- 1
 
0.4%
0 1
 
0.4%
1 1
 
0.4%
: 1
 
0.4%
Katakana
ValueCountFrequency (%)
2
25.0%
2
25.0%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
Han
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 723
98.4%
Katakana 8
 
1.1%
CJK 4
 
0.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
' 106
 
14.7%
[ 50
 
6.9%
] 50
 
6.9%
44
 
6.1%
u 28
 
3.9%
E 28
 
3.9%
i 24
 
3.3%
I 23
 
3.2%
a 23
 
3.2%
A 22
 
3.0%
Other values (46) 325
45.0%
Katakana
ValueCountFrequency (%)
2
25.0%
2
25.0%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
CJK
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%

danceability
Real number (ℝ)

Distinct48
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6042
Minimum0.31
Maximum0.901
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2023-11-24T10:50:02.533075image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.31
5-th percentile0.36535
Q10.49175
median0.611
Q30.7095
95-th percentile0.8021
Maximum0.901
Range0.591
Interquartile range (IQR)0.21775

Descriptive statistics

Standard deviation0.1370927
Coefficient of variation (CV)0.22689954
Kurtosis-0.47714405
Mean0.6042
Median Absolute Deviation (MAD)0.1115
Skewness-0.13294389
Sum30.21
Variance0.018794408
MonotonicityNot monotonic
2023-11-24T10:50:02.751795image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0.596 2
 
4.0%
0.649 2
 
4.0%
0.543 1
 
2.0%
0.48 1
 
2.0%
0.812 1
 
2.0%
0.598 1
 
2.0%
0.628 1
 
2.0%
0.367 1
 
2.0%
0.463 1
 
2.0%
0.54 1
 
2.0%
Other values (38) 38
76.0%
ValueCountFrequency (%)
0.31 1
2.0%
0.335 1
2.0%
0.364 1
2.0%
0.367 1
2.0%
0.424 1
2.0%
0.445 1
2.0%
0.457 1
2.0%
0.463 1
2.0%
0.479 1
2.0%
0.48 1
2.0%
ValueCountFrequency (%)
0.901 1
2.0%
0.853 1
2.0%
0.812 1
2.0%
0.79 1
2.0%
0.776 1
2.0%
0.769 1
2.0%
0.756 1
2.0%
0.737 1
2.0%
0.736 1
2.0%
0.733 1
2.0%

energy
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.76796
Minimum0.289
Maximum0.987
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2023-11-24T10:50:02.988169image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.289
5-th percentile0.5215
Q10.654
median0.785
Q30.90025
95-th percentile0.9705
Maximum0.987
Range0.698
Interquartile range (IQR)0.24625

Descriptive statistics

Standard deviation0.154169
Coefficient of variation (CV)0.20075134
Kurtosis0.3010241
Mean0.76796
Median Absolute Deviation (MAD)0.119
Skewness-0.61674924
Sum38.398
Variance0.02376808
MonotonicityNot monotonic
2023-11-24T10:50:03.217792image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.975 1
 
2.0%
0.965 1
 
2.0%
0.795 1
 
2.0%
0.669 1
 
2.0%
0.64 1
 
2.0%
0.511 1
 
2.0%
0.775 1
 
2.0%
0.671 1
 
2.0%
0.603 1
 
2.0%
0.958 1
 
2.0%
Other values (40) 40
80.0%
ValueCountFrequency (%)
0.289 1
2.0%
0.511 1
2.0%
0.517 1
2.0%
0.527 1
2.0%
0.578 1
2.0%
0.597 1
2.0%
0.598 1
2.0%
0.603 1
2.0%
0.628 1
2.0%
0.632 1
2.0%
ValueCountFrequency (%)
0.987 1
2.0%
0.977 1
2.0%
0.975 1
2.0%
0.965 1
2.0%
0.959 1
2.0%
0.958 1
2.0%
0.946 1
2.0%
0.943 1
2.0%
0.941 1
2.0%
0.94 1
2.0%

key
Real number (ℝ)

ZEROS 

Distinct11
Distinct (%)22.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.24
Minimum0
Maximum11
Zeros3
Zeros (%)6.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2023-11-24T10:50:03.416373image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.45
Q12.5
median5
Q37
95-th percentile10
Maximum11
Range11
Interquartile range (IQR)4.5

Descriptive statistics

Standard deviation3.1269728
Coefficient of variation (CV)0.59675054
Kurtosis-0.85851301
Mean5.24
Median Absolute Deviation (MAD)2
Skewness0.01831537
Sum262
Variance9.7779592
MonotonicityNot monotonic
2023-11-24T10:50:03.579491image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
5 9
18.0%
10 7
14.0%
7 7
14.0%
6 6
12.0%
1 6
12.0%
4 5
10.0%
2 4
8.0%
0 3
 
6.0%
11 1
 
2.0%
8 1
 
2.0%
ValueCountFrequency (%)
0 3
 
6.0%
1 6
12.0%
2 4
8.0%
4 5
10.0%
5 9
18.0%
6 6
12.0%
7 7
14.0%
8 1
 
2.0%
9 1
 
2.0%
10 7
14.0%
ValueCountFrequency (%)
11 1
 
2.0%
10 7
14.0%
9 1
 
2.0%
8 1
 
2.0%
7 7
14.0%
6 6
12.0%
5 9
18.0%
4 5
10.0%
2 4
8.0%
1 6
12.0%

loudness
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-4.84132
Minimum-11.86
Maximum-0.425
Zeros0
Zeros (%)0.0%
Negative50
Negative (%)100.0%
Memory size532.0 B
2023-11-24T10:50:03.775713image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-11.86
5-th percentile-8.94855
Q1-5.9185
median-4.4905
Q3-3.604
95-th percentile-2.20585
Maximum-0.425
Range11.435
Interquartile range (IQR)2.3145

Descriptive statistics

Standard deviation2.1678505
Coefficient of variation (CV)-0.44778087
Kurtosis1.81283
Mean-4.84132
Median Absolute Deviation (MAD)1.056
Skewness-1.0886908
Sum-242.066
Variance4.6995758
MonotonicityNot monotonic
2023-11-24T10:50:04.014952image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.425 1
 
2.0%
-2.727 1
 
2.0%
-4.778 1
 
2.0%
-5.168 1
 
2.0%
-8.034 1
 
2.0%
-6.015 1
 
2.0%
-4.479 1
 
2.0%
-4.604 1
 
2.0%
-5.464 1
 
2.0%
-4.26 1
 
2.0%
Other values (40) 40
80.0%
ValueCountFrequency (%)
-11.86 1
2.0%
-10.157 1
2.0%
-9.453 1
2.0%
-8.332 1
2.0%
-8.203 1
2.0%
-8.034 1
2.0%
-6.983 1
2.0%
-6.533 1
2.0%
-6.49 1
2.0%
-6.362 1
2.0%
ValueCountFrequency (%)
-0.425 1
2.0%
-1.66 1
2.0%
-2.146 1
2.0%
-2.279 1
2.0%
-2.595 1
2.0%
-2.692 1
2.0%
-2.727 1
2.0%
-3.176 1
2.0%
-3.193 1
2.0%
-3.223 1
2.0%

mode
Categorical

Distinct2
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
1
39 
0
11 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters50
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 39
78.0%
0 11
 
22.0%

Length

2023-11-24T10:50:04.222789image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-24T10:50:04.454975image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
1 39
78.0%
0 11
 
22.0%

Most occurring characters

ValueCountFrequency (%)
1 39
78.0%
0 11
 
22.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 50
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 39
78.0%
0 11
 
22.0%

Most occurring scripts

ValueCountFrequency (%)
Common 50
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 39
78.0%
0 11
 
22.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 50
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 39
78.0%
0 11
 
22.0%

speechiness
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.06718
Minimum0.0251
Maximum0.253
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2023-11-24T10:50:04.706632image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.0251
5-th percentile0.027645
Q10.03345
median0.04755
Q30.086575
95-th percentile0.1723
Maximum0.253
Range0.2279
Interquartile range (IQR)0.053125

Descriptive statistics

Standard deviation0.050463832
Coefficient of variation (CV)0.75117345
Kurtosis3.8020781
Mean0.06718
Median Absolute Deviation (MAD)0.0153
Skewness1.9800097
Sum3.359
Variance0.0025465984
MonotonicityNot monotonic
2023-11-24T10:50:05.028147image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.208 1
 
2.0%
0.0905 1
 
2.0%
0.0284 1
 
2.0%
0.0431 1
 
2.0%
0.0564 1
 
2.0%
0.0423 1
 
2.0%
0.0535 1
 
2.0%
0.0478 1
 
2.0%
0.0276 1
 
2.0%
0.0951 1
 
2.0%
Other values (40) 40
80.0%
ValueCountFrequency (%)
0.0251 1
2.0%
0.0273 1
2.0%
0.0276 1
2.0%
0.0277 1
2.0%
0.0284 1
2.0%
0.0298 1
2.0%
0.0305 1
2.0%
0.0308 1
2.0%
0.0319 1
2.0%
0.032 1
2.0%
ValueCountFrequency (%)
0.253 1
2.0%
0.208 1
2.0%
0.175 1
2.0%
0.169 1
2.0%
0.163 1
2.0%
0.125 1
2.0%
0.113 1
2.0%
0.11 1
2.0%
0.103 1
2.0%
0.0955 1
2.0%

acousticness
Real number (ℝ)

Distinct49
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.15033969
Minimum2.77 × 10-5
Maximum0.826
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2023-11-24T10:50:05.297689image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum2.77 × 10-5
5-th percentile0.0023665
Q10.030625
median0.07425
Q30.234
95-th percentile0.4734
Maximum0.826
Range0.8259723
Interquartile range (IQR)0.203375

Descriptive statistics

Standard deviation0.18463683
Coefficient of variation (CV)1.228131
Kurtosis4.4992596
Mean0.15033969
Median Absolute Deviation (MAD)0.06185
Skewness2.0310524
Sum7.5169847
Variance0.034090761
MonotonicityNot monotonic
2023-11-24T10:50:05.535352image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0.117 2
 
4.0%
0.172 1
 
2.0%
0.305 1
 
2.0%
0.0693 1
 
2.0%
0.0875 1
 
2.0%
0.245 1
 
2.0%
0.159 1
 
2.0%
0.222 1
 
2.0%
0.495 1
 
2.0%
0.0492 1
 
2.0%
Other values (39) 39
78.0%
ValueCountFrequency (%)
2.77 × 10-51
2.0%
0.000147 1
2.0%
0.00157 1
2.0%
0.00334 1
2.0%
0.0068 1
2.0%
0.0103 1
2.0%
0.0107 1
2.0%
0.0114 1
2.0%
0.0134 1
2.0%
0.0142 1
2.0%
ValueCountFrequency (%)
0.826 1
2.0%
0.763 1
2.0%
0.495 1
2.0%
0.447 1
2.0%
0.433 1
2.0%
0.343 1
2.0%
0.312 1
2.0%
0.305 1
2.0%
0.298 1
2.0%
0.27 1
2.0%

instrumentalness
Real number (ℝ)

ZEROS 

Distinct17
Distinct (%)34.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.016157405
Minimum0
Maximum0.8
Zeros34
Zeros (%)68.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2023-11-24T10:50:05.741357image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34.17 × 10-6
95-th percentile0.000328
Maximum0.8
Range0.8
Interquartile range (IQR)4.17 × 10-6

Descriptive statistics

Standard deviation0.1131182
Coefficient of variation (CV)7.001013
Kurtosis49.992796
Mean0.016157405
Median Absolute Deviation (MAD)0
Skewness7.0703228
Sum0.80787026
Variance0.012795728
MonotonicityNot monotonic
2023-11-24T10:50:05.943003image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 34
68.0%
1.62 × 10-61
 
2.0%
0.000317 1
 
2.0%
3.44 × 10-51
 
2.0%
0.000337 1
 
2.0%
0.000183 1
 
2.0%
3.24 × 10-61
 
2.0%
1.7 × 10-51
 
2.0%
5.83 × 10-51
 
2.0%
0.00029 1
 
2.0%
Other values (7) 7
 
14.0%
ValueCountFrequency (%)
0 34
68.0%
1.38 × 10-61
 
2.0%
1.62 × 10-61
 
2.0%
3.24 × 10-61
 
2.0%
4.48 × 10-61
 
2.0%
5.74 × 10-61
 
2.0%
1.34 × 10-51
 
2.0%
1.37 × 10-51
 
2.0%
1.7 × 10-51
 
2.0%
3.44 × 10-51
 
2.0%
ValueCountFrequency (%)
0.8 1
2.0%
0.00659 1
2.0%
0.000337 1
2.0%
0.000317 1
2.0%
0.00029 1
2.0%
0.000183 1
2.0%
5.83 × 10-51
2.0%
3.44 × 10-51
2.0%
1.7 × 10-51
2.0%
1.37 × 10-51
2.0%

liveness
Real number (ℝ)

UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.213378
Minimum0.0517
Maximum0.653
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2023-11-24T10:50:06.196976image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.0517
5-th percentile0.057765
Q10.10325
median0.1635
Q30.31775
95-th percentile0.42305
Maximum0.653
Range0.6013
Interquartile range (IQR)0.2145

Descriptive statistics

Standard deviation0.13496791
Coefficient of variation (CV)0.63252966
Kurtosis0.73694594
Mean0.213378
Median Absolute Deviation (MAD)0.0928
Skewness0.92737556
Sum10.6689
Variance0.018216338
MonotonicityNot monotonic
2023-11-24T10:50:06.414106image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.143 1
 
2.0%
0.366 1
 
2.0%
0.16 1
 
2.0%
0.149 1
 
2.0%
0.396 1
 
2.0%
0.0732 1
 
2.0%
0.33 1
 
2.0%
0.494 1
 
2.0%
0.167 1
 
2.0%
0.308 1
 
2.0%
Other values (40) 40
80.0%
ValueCountFrequency (%)
0.0517 1
2.0%
0.0529 1
2.0%
0.0534 1
2.0%
0.0631 1
2.0%
0.0683 1
2.0%
0.0731 1
2.0%
0.0732 1
2.0%
0.0797 1
2.0%
0.0856 1
2.0%
0.0859 1
2.0%
ValueCountFrequency (%)
0.653 1
2.0%
0.494 1
2.0%
0.437 1
2.0%
0.406 1
2.0%
0.396 1
2.0%
0.366 1
2.0%
0.347 1
2.0%
0.339 1
2.0%
0.332 1
2.0%
0.33 1
2.0%

valence
Real number (ℝ)

Distinct49
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.63378
Minimum0.282
Maximum0.962
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2023-11-24T10:50:06.623088image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.282
5-th percentile0.3314
Q10.521
median0.643
Q30.74775
95-th percentile0.8826
Maximum0.962
Range0.68
Interquartile range (IQR)0.22675

Descriptive statistics

Standard deviation0.1677243
Coefficient of variation (CV)0.2646412
Kurtosis-0.52717832
Mean0.63378
Median Absolute Deviation (MAD)0.117
Skewness-0.2433538
Sum31.689
Variance0.02813144
MonotonicityNot monotonic
2023-11-24T10:50:06.861621image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0.652 2
 
4.0%
0.742 1
 
2.0%
0.686 1
 
2.0%
0.577 1
 
2.0%
0.378 1
 
2.0%
0.893 1
 
2.0%
0.674 1
 
2.0%
0.691 1
 
2.0%
0.477 1
 
2.0%
0.609 1
 
2.0%
Other values (39) 39
78.0%
ValueCountFrequency (%)
0.282 1
2.0%
0.308 1
2.0%
0.317 1
2.0%
0.349 1
2.0%
0.378 1
2.0%
0.381 1
2.0%
0.424 1
2.0%
0.451 1
2.0%
0.477 1
2.0%
0.502 1
2.0%
ValueCountFrequency (%)
0.962 1
2.0%
0.893 1
2.0%
0.888 1
2.0%
0.876 1
2.0%
0.872 1
2.0%
0.844 1
2.0%
0.831 1
2.0%
0.817 1
2.0%
0.816 1
2.0%
0.807 1
2.0%

tempo
Real number (ℝ)

UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129.83284
Minimum75.093
Maximum194.084
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2023-11-24T10:50:07.274489image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum75.093
5-th percentile85.13225
Q1108.5735
median128.526
Q3149.463
95-th percentile177.68695
Maximum194.084
Range118.991
Interquartile range (IQR)40.8895

Descriptive statistics

Standard deviation28.032249
Coefficient of variation (CV)0.21591031
Kurtosis-0.31345216
Mean129.83284
Median Absolute Deviation (MAD)20.451
Skewness0.22076957
Sum6491.642
Variance785.80697
MonotonicityNot monotonic
2023-11-24T10:50:07.533052image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
132.054 1
 
2.0%
170.051 1
 
2.0%
130.037 1
 
2.0%
155.89 1
 
2.0%
116.992 1
 
2.0%
171.845 1
 
2.0%
106.382 1
 
2.0%
179.833 1
 
2.0%
148.089 1
 
2.0%
93.852 1
 
2.0%
Other values (40) 40
80.0%
ValueCountFrequency (%)
75.093 1
2.0%
78.004 1
2.0%
78.047 1
2.0%
93.792 1
2.0%
93.852 1
2.0%
98.047 1
2.0%
100.011 1
2.0%
102.983 1
2.0%
103.968 1
2.0%
106.017 1
2.0%
ValueCountFrequency (%)
194.084 1
2.0%
184.886 1
2.0%
179.833 1
2.0%
175.064 1
2.0%
171.845 1
2.0%
170.051 1
2.0%
165.783 1
2.0%
157.038 1
2.0%
155.89 1
2.0%
152.014 1
2.0%

duration_ms
Real number (ℝ)

UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean234052.12
Minimum151016
Maximum353340
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2023-11-24T10:50:07.775943image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum151016
5-th percentile168891.8
Q1199681.25
median229044
Q3268297
95-th percentile306365.9
Maximum353340
Range202324
Interquartile range (IQR)68615.75

Descriptive statistics

Standard deviation45759.83
Coefficient of variation (CV)0.19551128
Kurtosis0.038297902
Mean234052.12
Median Absolute Deviation (MAD)32263.5
Skewness0.51503243
Sum11702606
Variance2.093962 × 109
MonotonicityNot monotonic
2023-11-24T10:50:08.017089image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
189773 1
 
2.0%
192400 1
 
2.0%
289720 1
 
2.0%
343253 1
 
2.0%
151016 1
 
2.0%
219253 1
 
2.0%
278119 1
 
2.0%
230600 1
 
2.0%
286360 1
 
2.0%
191105 1
 
2.0%
Other values (40) 40
80.0%
ValueCountFrequency (%)
151016 1
2.0%
154667 1
2.0%
159080 1
2.0%
180884 1
2.0%
182224 1
2.0%
183551 1
2.0%
189773 1
2.0%
191105 1
2.0%
192400 1
2.0%
194267 1
2.0%
ValueCountFrequency (%)
353340 1
2.0%
343253 1
2.0%
307067 1
2.0%
305509 1
2.0%
289720 1
2.0%
288100 1
2.0%
288013 1
2.0%
286360 1
2.0%
286000 1
2.0%
278119 1
2.0%

time_signature
Categorical

IMBALANCE 

Distinct2
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
4
48 
3
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters50
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
4 48
96.0%
3 2
 
4.0%

Length

2023-11-24T10:50:08.234038image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-24T10:50:08.380652image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
4 48
96.0%
3 2
 
4.0%

Most occurring characters

ValueCountFrequency (%)
4 48
96.0%
3 2
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 50
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 48
96.0%
3 2
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
Common 50
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 48
96.0%
3 2
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 50
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 48
96.0%
3 2
 
4.0%

Interactions

2023-11-24T10:49:57.106502image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:38.877682image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:40.743871image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:42.328356image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:43.870913image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:45.574830image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:47.228209image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:48.821764image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:50.757576image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:52.371159image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:55.271330image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:57.276266image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:39.085759image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:40.894036image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:42.458352image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:44.011461image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:45.723742image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:47.370548image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:48.982502image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:50.905922image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:52.527167image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:55.426291image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:57.441147image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:39.260019image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:41.041091image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:42.648578image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:44.189590image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:45.875104image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:47.510332image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:49.142826image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:51.062807image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:52.691748image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:55.619133image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:57.630444image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:39.409237image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:41.187773image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:42.774558image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:44.330493image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:46.014595image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:47.640368image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:49.301569image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:51.217847image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:52.833387image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:55.757381image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:57.865021image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:39.631230image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:41.325157image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:42.906869image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:44.468429image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:46.158112image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:47.774172image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:49.460694image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:51.367392image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:52.982033image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:55.921832image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:58.054535image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:39.818069image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:41.453946image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:43.032840image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:44.658225image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:46.283712image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:47.900534image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:49.619964image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:51.500407image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:54.248850image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:56.055552image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:58.295666image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:39.982220image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:41.595428image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:43.158231image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:44.816719image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:46.413104image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:48.029857image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:49.828300image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:51.639833image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:54.425951image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:56.186905image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:58.485305image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:40.142687image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:41.759369image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:43.303494image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:44.975927image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:46.573816image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:48.190299image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:50.008941image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:51.798708image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:54.573097image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:56.402061image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:58.652727image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:40.293220image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:41.895281image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:43.427598image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:45.111893image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:46.717379image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:48.335679image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:50.164202image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:51.935533image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:54.779018image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:56.583696image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:58.811097image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:40.439422image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:42.033603image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:43.575683image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:45.270320image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:46.884180image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:48.521576image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:50.412793image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:52.074750image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:54.955598image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:56.721588image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:58.971071image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:40.587200image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:42.176881image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:43.719666image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:45.408745image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:47.073551image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:48.667936image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:50.570494image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:52.217707image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:55.100768image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T10:49:56.861135image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-11-24T10:50:08.495044image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
acousticnessdanceabilityduration_msenergyinstrumentalnesskeylivenessloudnessmodespeechinesstempotime_signaturevalence
acousticness1.0000.285-0.109-0.445-0.1180.282-0.356-0.2790.000-0.253-0.0260.000-0.075
danceability0.2851.000-0.431-0.1670.1180.074-0.347-0.0990.3000.036-0.3460.0000.314
duration_ms-0.109-0.4311.000-0.331-0.1390.024-0.074-0.2920.000-0.3390.1490.000-0.386
energy-0.445-0.167-0.3311.0000.1520.0360.4660.7740.2020.5770.1280.0000.253
instrumentalness-0.1180.118-0.1390.1521.000-0.0130.115-0.0590.0000.1050.1760.0000.115
key0.2820.0740.0240.036-0.0131.000-0.2570.1530.0000.017-0.0510.000-0.137
liveness-0.356-0.347-0.0740.4660.115-0.2571.0000.2910.0000.4260.1890.0000.145
loudness-0.279-0.099-0.2920.774-0.0590.1530.2911.0000.1560.3610.0950.0000.232
mode0.0000.3000.0000.2020.0000.0000.0000.1561.000-0.309-0.0320.000-0.293
speechiness-0.2530.036-0.3390.5770.1050.0170.4260.361-0.3091.0000.1500.0000.232
tempo-0.026-0.3460.1490.1280.176-0.0510.1890.095-0.0320.1501.0000.117-0.057
time_signature0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1171.0000.007
valence-0.0750.314-0.3860.2530.115-0.1370.1450.232-0.2930.232-0.0570.0071.000

Missing values

2023-11-24T10:49:59.251328image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-24T10:49:59.583154image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Track_IDTrack_NameTrack_Artistsdanceabilityenergykeyloudnessmodespeechinessacousticnessinstrumentalnesslivenessvalencetempoduration_mstime_signature
01rDgAHDX95RmylxjgVW9tNShow['Ado']0.6160.97510-0.42500.20800.172000.0000020.14300.742132.0541897734
10GWNtMohuYUEHVZ40tcnHFSPECIALZ['King Gnu']0.5660.8506-4.43100.05990.066300.0000000.31900.722117.0482389874
22HRgqmZQC0MC7GeNuDIXHNSeven (feat. Latto) (Explicit Ver.)['Jung Kook', 'Latto']0.7900.83111-4.18510.04400.312000.0000000.07970.872124.9871835514
35i8lwhzx9FyilInJWa5lhnrendez-vous['shy taupe']0.4970.6492-4.76010.03250.117000.0000000.17000.317146.0562377404
41hAloWiinXLPQUJxrJReb1アイドル['YOASOBI']0.4570.94110-2.69200.09170.105000.0000010.43700.876165.7832117334
574X2u8JMVooG2QbjRxXwR8Perfect Night['LE SSERAFIM']0.6970.8205-4.50210.03080.100000.0000000.06310.502136.0541590804
60YTM7bCx451c6LQbkddy4Q勇者['YOASOBI']0.6550.9075-3.86810.04880.035100.0000140.15000.506103.9681942674
73gQ19Wo6CbBpdHYmt2GVt0Kaiju no Hanauta['Vaundy']0.3350.9432-3.17610.06700.003340.0000000.32400.64075.0932248054
812usPU2WnqgCHAW1EK2dfd青のすみか['Tatsuya Kitani']0.4790.8981-4.80910.16900.013400.0000000.29700.639151.9901964674
902tNuntKQsoou5T4O8meyhHana['Fujii Kaze']0.7690.7165-8.20300.04290.118000.0065900.15800.660123.0312467004
Track_IDTrack_NameTrack_Artistsdanceabilityenergykeyloudnessmodespeechinessacousticnessinstrumentalnesslivenessvalencetempoduration_mstime_signature
402Dzzhb1oV5ckgOjWZLraIBOverdose['natori']0.7330.5984-9.45300.03770.0307000.0000170.21800.831118.0121970944
416A8NfypDHuwiLWbo4a1ycaNew Genesis['Ado']0.4900.9877-2.27910.25300.0490000.0000030.40600.451175.0642268134
426twDMJoG8tzwL21LQ3EEtAMainstream['BE:FIRST']0.9010.71410-3.91810.12500.2460000.0000000.05290.520100.0112070534
436rFRQKFyluXEJhM5ANu2XBNichijo['OFFICIAL HIGE DANDISM']0.6060.7081-6.98310.03420.0205000.0001830.17300.758117.9823533404
446wffxmLgeZTbvS1hYvLkhtTattoo['OFFICIAL HIGE DANDISM']0.4810.9010-5.62910.16300.0114000.0000000.31400.817194.0842881004
452vMc8rqFmqs7RFi8NDx0CJGarden['Fujii Kaze']0.5890.5274-8.33210.05720.0932000.0003370.10400.576138.0232293334
465sdQOyqq2IDhvmx2lHOpwdSuper Shy['NewJeans']0.7760.8175-6.01800.07480.1800000.0000340.14600.515149.9211546674
476x7SB38tuekpu4xpH9OIPY地球儀 - Spinning Globe['Kenshi Yonezu']0.6420.28910-11.86010.04720.7630000.0000000.08560.28278.0042734384
486zjk7Qbwb9DZ4ykUUoqknh第ゼロ感['10-FEET']0.4450.9407-4.16010.17500.0000280.0000130.32000.349150.0822860004
497jtXjZ3JViUo9M3ogYAapO['Soushi Sakiyama']0.6450.7205-4.51510.03550.2700000.0000000.27700.634127.0522391074